X-INtelligent Grid Laboratory
XING ("Crossing") seeks to create and innovate cross-disciplinary solutions for next-generation smart grids, overlapping socio-economics, meteorology, AI, and machine learning to address compound real-world needs. Modern power systems are confronted with degraded and even stranded operational performances subject to growing complexity, uncertainty, and volatility from large-scale integration of renewable and distributed energy resources (DERs). Toward next-generation intelligent grids, our mission is to bridge the gap between energy system research and state-of-the-art data science by utilizing diversified data from weather and renewable generation data, phasor measurement units (PMUs), advanced metering infrastructure (AMI) systems, etc.
Prof. Ying Zhang
I am currently an Assistant Professor in the School of Electrical and Computer Engineering at Oklahoma State University, Stillwater, OK, U.S. I received my Ph.D. degree in Electrical Engineering at Southern Methodist University (SMU), Dallas, TX, U.S. Before joining OSU, I was a postdoctoral research associate in the U.S. DOE’s Brookhaven National Laboratory (BNL) with the Interdisciplinary Science Department, an Assistant Professor at Montana State University, and a Visiting Scholar in Cornell University. I received the National Scholarship for Highest Academic Distinction in China, the Frederick E. Terman Award for Graduate Students at SMU, the inaugural IEEE Power and Energy Society Outstanding Doctoral Dissertation Award (one of four awardees for the Ph.D. students in power and energy systems who graduated in 2020-2022). My publications have received distinctions such as multiple Best Paper Awards at the IEEE PES General Meeting and IEEE PES Innovative Smart Grid Technologies Conference. My research is rooted in power system situational awareness via optimization, machine learning, and artificial intelligence (AI) to develop grid-based interdisciplinary research in climate change and data science.
Research Interests
Energy and Power Systems
Renewable Integration
AI for Energy
Smart Grid and Active Distribution Networks
Resilience and Equity Development Against Climate Change
News
10/2024: Dr. Zhang gave an invited talk at the 2024 INFORMS Annual Meeting, titled "Physics-Informed Machine Learning to Enhance Distribution Grid Situational Awareness".
10/2024: Our Paper, Knowledge-Inspired Data-Aided Robust Power Flow in Distribution Networks with ZIP Loads and High DER Penetration, is accepted by IEEE Transactions on Industrial Applications (IF: 4.2). Congrats to Sungjoo and Yuanshuo!
09/2024: Undergraduate student Luke Dwayne Cardiel joined the lab as a Research Assistant. Welcome! Luke has been selected as an ECE Miller Research Scholar for the 2024-2025 academic year, based on his excellent research proposal under Dr. Zhang's advising. Congrats, Luke!
07/2024: [Breaking News] We have been awarded a $6 million grant for our NSF project, "RII FEC: Accelerating Community-Centric Energy Transformation through AI-driven Digital Twinning for Climate-Aware Resilience." Dr. Zhang will co-lead as OSU PI and work with NMSU, the University of Alabama Huntsville, and MSU to address the critical challenges of climate change and aging energy infrastructure, with a focus on underserved communities. Kudos to the team! [See News Release]
07/2024: Dr. Zhang is recognized as the secretary of the IEEE Task Force on Performance Evaluation for Distribution System State Estimation.
07/2024: [Breaking News] Ph.D. student Sungjoo Chung won First Prize in the Graduate Student Poster Contest at the IEEE Power and Energy Society General Meeting, for our poster titled "Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method." Congrats to Sungjoo!
05/2024: [Breaking News] Our Paper, with Ph.D. student Sungjoo Chung as the first author, Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method, won the Best Paper award at the 2024 IEEE Power and Energy Society (PES) General Meeting. Congrats to the team! [See News Release]
04/2024: Three ECE students, Taha Saeed Khan (Advisor: Dr. Nazaripouya) , Ahmad Ali (Advisor: Dr. Cui ), and Sungjoo Chung (Advisor: Dr. Y. Zhang), won the “Best in Group” Award in the OSU 2nd CEAT Graduate Student Research Symposium. Congrats to Taha, Ahmad, and Sungjoo.
04/2024: Sungjoo received the 2024 Dr. Ramakumar Family Energy Scholarship sponsored by the College of Engineering, Architecture and Technology at OSU as one of the two runners-up. Congrats, Sungjoo!
03/2024: Our paper, Addressing Wind Power Forecast Errors in Day-Ahead Pricing With Energy Storage Systems: A Distributionally Robust Joint Chance-Constrained Approach, is accepted by IEEE Transactions on Sustainable Energy (IF: 8.8).
02/2024: Our paper, Cooperative Multi-Agent Deep Reinforcement Learning for Adaptive Decentralized Emergency Voltage Control, is selected as the Best Paper Contest Finalist (Top 5) by the 2024 IEEE PES Innovative Smart Grid Technologies Conference!
02/2024: Our paper, Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method, is accepted by the IEEE PES General Meeting, a flagship conference in the domain of power and energy systems. Congrats to Sungjoo!
01/2024: Yuanshuo Zhang (23' CUHK) joined the lab as a Ph.D. student. Welcome!
12/2023: Dr. Zhang joined Oklahoma State University (OSU) as an Assistant Professor in the School of Electrical and Computer Engineering.
12/2023: Our paper, Multi-Agent Graph-Attention Deep Reinforcement Learning for Post-Contingency Grid Emergency Voltage Control, is accepted by IEEE Transactions on Neural Networks and Learning Systems (IF: 10.4).
11/2023: Dr. Zhang is appointed Associate Editor of the journal IET Generation, Transmission & Distribution.
10/2023: Dr. Zhang is appointed Vice Chair of the Awards Subcommittee, IEEE Power and Energy Society (PES) Power System Operation, Planning and Economics Committee (2024-2028).
10/2023: Two papers, Deep Reinforcement Learning-Enabled Adaptive Forecasting-Aided State Estimation in Distribution Systems with Multi-Source Multi-Rate Data & Cooperative Multi-Agent Deep Reinforcement Learning for Adaptive Decentralized Emergency Voltage Control, are accepted by the 2024 IEEE IEEE PES Innovative Smart Grid Technologies Conference.
07/2023: Invited talk in the Outstanding Dissertation Award Panel of the 2023 IEEE PES General Meeting, "Distribution System Situational Awareness: From Model-Based to Data-Driven and Beyond", please see the slides here . Dr. Zhang's dissertation was officially awarded as the 2023 IEEE Power and Energy Society Outstanding Doctoral Dissertation to recognize the research achievement of Ph.D. students who graduated in 2020-2022 in the power and energy domain.
06/2023: Our paper, "Artificial Intelligence Applications in Electric Distribution Systems: Post-Pandemic Progress and Prospect", is accepted by Applied Sciences. Congrats Sungjoo!
05/2023: Dr. Zhang started a one-month summer visit at Cornell University, a lot of brainstorming and discussion with Dr. Hsiao-Dong Chiang! Thanks for Dr. Chiang's hospitality.
03/2023: Invited Talk in Women in Data Science 2023 @ University of Calgary, Calgary, Canada. Happy International Women's Day!
01/2023: Sungjoo Chung (22' UIUC) joined the lab as a Ph.D. student. Welcome!
01/2023: Our paper, Off-policy deep reinforcement learning with automatic entropy adjustment for adaptive grid emergency control, is accepted by Electric Power Systems Research!
08/2022: Dr. Ying Zhang joined MSU as an Assistant Professor, in the Electrical and Computer Engineering Department, affiliated with MSU's Energy Research Institute.
